It’s Components All The Way Down

• Cooperate to Compete: The Business Advantage of Components
• Get Unstuck: Using Component Thinking to Drive Modernization
• Don't Build a Better Chore Factory
• Modern Expectations Are Driving New UX Patterns

UX Design for Modern Financial Services Applications

• What is a Design-First Approach?
• How Do I Simplify Workflows?
• What are the technology obstacles to modernization?
• How do users interact with Analytics?

Using UI Prototypes to Validate Your Product

• Deep UX thinking
• Rapid prototyping
• User testing
• Live demo

Best Practices for Creating Useful & Usable Interactive Visualizations

• Data visualization
• Product selection
• Visualization types
• Deep UX thinking

UI Design Patterns for Navigating Complex Data Sets

• Increased eficiency
• Deep UX Thinking
• Brand strengthening
• UI pattern libraries

Financial Services Toolkit: Dashboard Demo

• Transaction History and Recommendations
• Items for Compliance and Shareable Elements
• 'What If' Portfolio Analysis
• Asset Correlations and Visualizations for Recommendation

Financial Services Toolkit: Portfolio Demo

• An In-Depth Look into the Performance Recommendation Widget
• 'What If' Scenario Building
• Sharing Interactions with Jupyter Notebook
• Running and Editing Scenarios in Jupyter Notebook

Fleet Management & Scheduling

• Routing
• Optimization
• Route Recommendations
• Machine Learning & Analytics

Road Risk & Driver Safety Using IoT Data with Moove.ai

• Real Time IoT
• Machine Learning & Analytics
• Risk Modeling
• Fleet & Auto

Financial Crimes Investigation Workbench

• Financial Crime
• ML & Graph Technology
• Investigation Workbench
• Deep Link Analytics

Chatting with Data: Interrogative UIs for Exploratory Data Analytics

Natural language processing is a powerful tool in building investigatory user interfaces for data exploration. See how conversation UI can be used to interrogate highly connected data to produce context driven data visualization and analytics.

UX Design for Data Products

• Improving data products through UX design
• Understanding, trusting, & taking action on data
• Workflows, information hierarchy, & design systems
• Testing designs to gain insights and iterate